Iron Metabolism and Idiopathic Pulmonary Arterial Hypertension: New Insights from Bioinformatic Analysis

Idiopathic pulmonary arterial hypertension (IPAH) is a rare vascular disease with a poor prognosis, and the mechanism of its development remains unclear. Further molecular pathology studies may contribute to a comprehensive understanding of IPAH and provide new insights into diagnostic markers and potential therapeutic targets. Iron deficiency has been reported in 43-63% of patients with IPAH and is associated with reduced exercise capacity and higher mortality, suggesting that dysregulated iron metabolism may play an unrecognized role in influencing the development of IPAH. In this study, we explored the regulatory mechanisms of iron metabolism in IPAH by bioinformatic analysis. The molecular function of iron metabolism-related genes (IMRGs) is mainly enriched in active transmembrane transporter activity, and they mainly affect the biological process of response to oxidative stress. Ferroptosis and fluid shear stress and atherosclerosis pathways may be the critical pathways regulating iron metabolism in IPAH. We further identified 7 key genes (BCL2, GCLM, MSMO1, SLC7A11, SRXN1, TSPAN5, and TXNRD1) and 5 of the key genes (BCL2, MSMO1, SLC7A11, TSPAN5, and TXNRD1) as target genes may be regulated by 6 dysregulated miRNAs (miR-483-5p, miR-27a-3p, miR-27b-3p, miR-26b-5p, miR-199a-5p, and miR-23b-3p) in IPAH. In addition, we predicted potential IPAH drugs—celastrol and cinnamaldehyde—that target iron metabolism based on our results. These results provide insights for further definition of the role of dysregulated iron metabolism in IPAH and contribute to a deeper understanding of the molecular mechanisms and potential therapeutic targets of IPAH.


Introduction
Pulmonary arterial hypertension (PAH) is a rare vascular disease with high morbidity and mortality, characterized by pulmonary vascular remodeling and increased pulmonary vascular resistance, ultimately resulting in right ventricular failure and death [1,2]. There are significant differences in the progression and prognosis between patients with PAH of different etiologies, ethnicities, and genetic mutations, suggesting that targeted therapies are necessary to improve the overall prognosis of patients [3][4][5].
Idiopathic PAH (IPAH) is a specific type of PAH without any family history of PAH or known pathogenic factors, and patients with IPAH tend to show worse survival compared to PAH associated with congenital heart disease [4,6]. Although the pulmonary hemodynamics, exercise capacity, and life quality of IPAH patients have improved considerably with advances in diagnosis and treatment, there is still no satisfactory cure available [6][7][8]. An essential understanding of the molecular and pathological mechanism may provide new insights for the therapy for IPAH.
Iron is an essential element in basic biological processes, which contributes to a multitude of crucial physiologic processes [9]. Iron deficiency has been reported in 43-63% of patients with IPAH and is associated with reduced exercise capacity and higher mortality [10][11][12]. Studies have shown that intracellular iron deficiency in pulmonary arterial smooth muscle cells could alter pulmonary vascular function, and rats on an iron-deficient diet exhibit significant pulmonary vascular remodeling with prominent muscularization, medial hypertrophy, and perivascular inflammatory cell infiltration, associated with elevated pulmonary artery pressure and right ventricular hypertrophy [13,14], which indicates iron metabolism participating in the maintenance of pulmonary vascular homeostasis, and dysregulated iron metabolism may play an important role in the development of IPAH.
Since current animal models provide little accurate information on the pathobiology of human IPAH and the value of developing and validating drug therapy is debatable, the research on human specimens should be paid more attention [6,15]. The advancement of gene microarray expression analysis has greatly contributed to the exploration of crucial genes in the pathobiology of IPAH [16,17], and microarray datasets from lung tissue may provide a more accurate and direct reflection of the pathobiology of IPAH than peripheral blood. Here, to determine the role of iron metabolism in IPAH, we identified iron metabolismrelated genes (IMRGs) based on relevant databases and analyzed the differential expression of IMRGs among IPAH and normal samples in the microarray dataset GSE117261. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses of differentially expressed IMRGs (DEIMRGs) were further performed, and proteinprotein interaction (PPI) network was constructed to identify key modules and hub genes. Studies have demonstrated that microRNAs (miRNAs) exert an essential effect on IPAH by negatively regulating target mRNA [15]; we constructed a miRNA-mRNA network to explore the potential regulation of IMRG by miRNAs in IPAH. The expression and diagnostic value of hub genes and target DEIMRGs (tIMRG) were further validated in the microarray dataset GSE15197 to identify key genes and crucial miRNA-IMRG networks. Finally, we predicted potential therapeutic drugs for IPAH based on our findings.

Materials and Methods
2.1. Data Collection. The microarray datasets GSE117261 and GSE15197 were downloaded from the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/ geo/). The dataset GSE117261 contains whole transcriptome expression data from 32 IPAH lung samples and 25 normal lung samples, used to screen for DEIMRGs. The dataset GSE15197 contains whole transcriptome expression data from 18 IPAH lung samples and 13 normal lung samples, used to verify the target DEIMRGs and hub genes. After ID conversion, the average expression value was taken as the gene expression value when multiple probes correspond to one gene. The raw data were log2 transformed and quantile normalized before analyses.

Identification of DEGs and DEIMRGs.
DEGs were identified using the limma package (version 3.48.0) in R software (version 4.1), p values were adjusted using the Benjamini and Hochberg method [18]. The false discovery rate ðFDRÞ < 0:05 and |log 2FC | ≥0:25 were defined as the selection thresholds for selecting the DEGs.

GO and KEGG Enrichment
Analyses. The DEIMRGs identified were subjected to GO and KEGG enrichment analysis. The clusterProfiler package (version 3.12.0) was used in R software to perform the GO and KEGG enrichment analyses. The results with FDR < 0:05 were considered significantly enriched by DEIMRGs.

Construction of DRmiRNA-DEIMRG Regulatory
Network. Dysregulated miRNAs (DRmiRNAs) in IPAH were extracted from previous studies. We searched the literature related to miRNAs and IPAH in the PubMed database (https://pubmed.ncbi.nlm.nih.gov/) [21] and excluding nonhuman specimen studies and studies without validation; a total of 23 DRmiRNAs were identified, 14 of which were upregulated and 9 downregulated, and the source studies and validation methods for each DRmiRNA are detailed in Table S2. The mirDIP database (http://ophid.utoronto.ca/ mirDIP/), an integrative database of human microRNA target predictions [22], was used to predict the target mRNAs of DRmiRNAs; target mRNAs with very high score class were selected. To visualize the relationship between DRmiRNAs and predicted target mRNAs, we built a miRNA-mRNA network using Cytoscape (version 3.8.2). tIMRGs were identified using the VennDiagram package (1.6.20) in R software.

PPI Network Construction and Identification of Key
Modules and Hub Genes. The STRING database (https:// string-db.org/) and Cytoscape software were used to construct a PPI network; PPI network of DEIMRGs was constructed using the STRING database and visualized in Cytoscape software. Three functional modules were identified by the Cytoscape plugin MCODE (the parameters were set to default: degree cutoff = 2, node score cutoff = 0:2, K − core = 3, and Max depth = 100). Another plugin, Cytohubba, was used to identify hub genes. The built-in MCC algorithm of Cytohubba assigned a value to each gene in 2 BioMed Research International the PPI network and ranked these genes by values; the top 10 genes were significant and regarded as hub genes.
2.6. Validation of Hub Genes and tIMRGs in GSE15197. The microarray dataset GSE15197 was used for validation. After data preprocessing as described previously, the expression data of hub genes and tIMRGs were extracted and groups were compared using the t-test; the results with p < 0:05 were considered statistically significant. Receiver operating characteristic (ROC) curve analyses were performed using the HiPlot software (version 0.1.0) to determine sensitivity and specificity of hub genes and tIMRGs; the multiple gene ROC analysis was performed based on the predictive probability of multiple genes for the outcome in each sample calculated by binary logistic regression using SPSS version 22.0. Results were quantified by the area under the ROC curve (AUC); genes with AUC > 0:6 were considered to have diagnostic value.

Immune Infiltration Analyses.
To estimate the proportion of infiltrating immune cells, normalized gene expression data of GSE117261 and GSE15197 were submitted to HiPlot software (version 0.1.0). The proportion of infiltrating immune cells was calculated with the CIBERSORT algorithm, t-test was used for comparison between groups, and linear regression analysis was used to analyze the correlation between gene expression and the proportion of immune cells. The results with p < 0:05 were defined as a statistically significant difference.

Overall
Protocol of the Study. The overall flowchart of the study is summarized in Figure 1. All the raw data were log-transformed and quantized before analysis, as shown in Figure S1.

Construction of PPI Network and Identification of Key
Modules and Hub Genes. To explore the interactions of these identified DEIMRGs, we constructed a PPI network (Figure 6(a)) of DEIMRGs using the STRING database. Further, we used Cytoscape software to analyze the data and identify key modules and hub genes. Finally, 3 key modules ( Figure S5) were identified, and TXNRD1, NQO1, G6PD, PRDX1, HMOX1, SRXN1, GCLM, SLC7A11, GPX2, and GCLC were selected as hub genes; the rank values of all DEIMRGs are listed in Table S4. The differential expression of hub genes in IPAH lung samples is shown in Figure 6(b), while the multiple associations between hub genes and with other DEIMRGs are shown in Figures 6(c) and 6(d). Interestingly, module 1 overlaps exactly with the hub genes we identified, which further demonstrates that these hub genes are the major functional clusters in DEIMRGs.

Immune Infiltration Analyses.
We performed an immune infiltration analysis in an attempt to explore the crosstalk between iron metabolism and immune responses in IPAH. The proportion of infiltrating immune cells of the samples from the GSE117261 and GSE15197 datasets was estimated by the CIBERSORT algorithm (Tables S5 and S6) and then visualized (Figures 8(a) and 8(b)). The clustering heat map showed the difference between IPAH and control lung samples of infiltrating immune cells in the two datasets (Figures 8(c) and 8(d)), and correlation heat maps showed correlations between different infiltrating immune cells ( Figure S6). In both datasets, the proportion of CD8 + T cells increased significantly in the IPAH samples and the proportion of neutrophils decreased significantly in the IPAH samples, while the other immune cells did not exhibit significant differences with a consistent trend (Figures 8(e) and 8(f)). The results of linear regression analysis showed that the expression of all key genes in both datasets did not show a significant correlation with the proportion of immune cells in the control samples (p > 0:05). As for IPAH samples, in the GSE117261 dataset, the expression of MSMO1 showed a significant positive correlation with the proportion of neutrophils, and the expression of TSPAN5 showed a significant positive correlation with the proportion of CD8 + T cells; in the GSE15197 dataset, the expression of GCLM, MSMO1, and TXNRD1 showed a significant negative correlation with the proportion of CD8 + T cells. Interestingly, none of the key genes we identified showed significant correlation with CD8 + T cells or neutrophils in both datasets. (Figure S7).

Targeted Drug Prediction.
We used the DSigDB database to predict potential target drugs which are related to key genes, which may potentially treat IPAH by modulating iron metabolism. Finally, 34 target drugs were predicted; combined score and corresponding target genes are listed in Table S7. Figure S8 shows the top 10 predicted target drugs ranked according to FDR; the top two drugs-celastrol (combined score = 12028) and cinnamaldehyde (combined score = 6513) have a strong drug-target correlation (FDR < 0:0001).

Discussion
For IPAH, as a poor prognosis type of PAH, none of the current therapies are actually curative [4,6,7]. However, targeted therapies for specific genes, such as BMPR2, in patients with IPAH have shown some encouraging results [24,25], indicating that further exploration of the molecular and pathological mechanisms of IPAH may provide promising therapeutic targets for patients. Dysregulated iron metabolism is closely associated with the development and progression of various cardiovascular diseases, including coronary artery disease, heart failure, and pulmonary hypertension [26]. A large proportion of patients with IPAH are characterized by iron deficiency, even without anemia, and associated with reduced exercise capacity and survival [10][11][12], suggesting that dysregulation of iron homeostasis may be a potential mechanism for the development and progression of IPAH. However, whether iron deficiency contributes to or is merely a consequence of IPAH remains debated; the mechanisms by which dysregulated iron 14 BioMed  The results of differential expression analysis showed that a significant proportion of IMRGs were differentially expressed in IPAH and normal lung samples. Further, GO enrichment analysis revealed that the molecular function of IMRGs is mainly enriched in active transmembrane transporter activity and mainly affects the biological process of response to oxidative stress. Several studies have indicated that there is an abnormal elevation of hepcidin in IPAH patients due to various factors such as BMPR2 mutation and inflammatory response, which can inhibit intestinal iron uptake and intracellular iron export, leading to circulating iron deficiency and intracellular iron overload [12,[27][28][29]. Intracellular iron overload is associated with mitochondrial dysfunction and production of reactive oxygen species and causes lipid peroxidation, DNA oxidation, and protein oxidation such as carbonylation, via the Fenton reaction and the Haber-Weiss pathway, and hence affects the cellular response to oxidative stress [27,[30][31][32], which has been 17 BioMed Research International demonstrated to be an important biological process involved in the progression of IPAH by affecting pulmonary vascular function and remodeling [33][34][35][36]. KEGG enrichment analysis identified pathways that may be involved in the regulation of DEIMRGs-ferroptosis and fluid shear stress and atherosclerosis pathway. Theoretically, the activation of ferroptosis pathway may be associated with iron-dependent lipid peroxidation induced by intracellular iron overload and lead to pulmonary vascular remodeling by affecting protein carbonylation [30,37,38], while fluid shear stress may cause vascular remodeling through iron-mediated generation of atherogenic mediators [39]. However, the role of these pathways involved in mediating iron metabolism dysregulation on the pathogenesis of IPAH needs to be further explored, as there are few relevant studies. miRNAs play an important regulatory role in the development of IPAH and have been demonstrated to be involved in the progression of IPAH by regulating the expression of target genes affecting metabolism and proliferation, DNA damage, vasoconstriction, and angiogenesis [40][41][42]. Iron ns ⁎ ns ns ⁎⁎ ns ⁎⁎⁎ ns ns ⁎ ns ns ⁎ ns ⁎⁎ ⁎ ⁎⁎ ⁎⁎⁎   5   10   15   ATP6V1A  BCL2  BTG2  FBXW7  G6PD  GCLC  GCLM  GPX2  HMOX1  MSMO1  NQO1  PRDX1  SCD  SLC25A37  SLC7A11  SRXN1  TSPAN5  Receiver operating characteristic (ROC) analysis showed the predictive performance of hub genes for IPAH in GSE15197. AUC: area under the ROC curve; DEIMRG: differentially expressed IMRG. * p < 0:05, * * p < 0:01, * * * p < 0:001, and * * * * p < 0:0001; ns: not significant. 18 BioMed Research International     GSE15197   GSM379342  GSM379348  GSM379349  GSM379353  GSM379338  GSM379343  GSM379345  GSM379344  GSM379340  GSM379321  GSM379341  GSM379322  GSM379320  GSM379327  GSM379328  GSM379351  GSM379317  GSM379324  GSM379323  GSM379325  GSM379352  GSM379337  GSM379346  GSM379350  GSM379318  GSM379339  GSM379326  GSM379354  GSM379319  GSM379347    23 BioMed Research International miRNA dysregulation are important regulators of IPAH development, we hypothesized that there is crosstalk between them in IPAH patients and then constructed a DRmiRNA-DEIMRG regulatory network and identified 12 tIMRGs. In addition, we identified 10 hub genes in DEIMRGs that are highly associated with other proteins through the construction of PPI networks. After validation of tIMRGs and hub genes in another independent dataset, we identified 7 key genes associated with iron metabolism. Intracellular iron overload leads to reduced expression of GCLM and SLC7A11, which consequently affects glutathione synthesis or intracellular unstable iron metabolism, resulting in cellular ferroptosis [49,50]. The NRF2 signaling pathway plays a critical role in mitigating lipid peroxidation and ferroptosis, whereas the downregulation of TXNRD1 and SRXN1, important signaling molecules in the NRF2 signaling pathway, may render cells more susceptible to ferroptosis. Although studies have reported increased expression of TXNRD1 and SRXN1 in protective iron overload heart and kidney tissues due to activation of the NRF2 signaling pathway, their expression was decreased in IPAH lung samples we analyzed, which may be due to much higher levels of iron overload and the presence of oxidative stress activated by other factors [51][52][53][54][55]. The increased expression of TSPAN5 may be related to the activation of the NOTCH signaling pathway by increased cellular uptake of iron, while the downregulation of MSMO1 expression may be mechanistically related to heme metabolism, but relevant studies are lacking [56][57][58][59]. In addition, intracellular iron overload usually leads to downregulation of BCL2 and induces apoptosis; interestingly, the expression of BCL2 was upregulated in the IPAH lung samples we analyzed, which may result in abnormal antiapoptotic phenotypic changes in pulmonary vascular endothelial cells and pulmonary vascular smooth muscle cells due to factors other than iron metabolism [60][61][62]. Five of the key genes (BCL2, MSMO1, SLC7A11, TSPAN5, and TXNRD1) as target genes may be regulated by 6 DRmiRNAs (miR-483-5p, miR-27a-3p, miR-27b-3p, miR-26b-5p, miR-199a-5p, and miR-23b-3p), which could be a potential crosstalk between iron metabolism and miRNA regulation in IPAH. Several key genes have been reported to be involved in the development of IPAH, but their iron metabolism-related regulatory mechanisms in IPAH patients remain unclear, as well as their regulation by miRNAs, which needs to be further explored [57].

GSE15197 B cells naive B cells memory Plasma cells T cells CD8 T cells CD4 naive T cells CD4 memory resting T cells CD4 memory activated T cells follicular helper T cells regulatory (tregs) T cells gamma delta
It is well known that immune and inflammatory responses play a crucial role in the pathogenesis of IPAH [63,64], while genetic and metabolic abnormalities are inextricably linked to dysregulated immunity and adverse remodeling in the pulmonary arteries [65]. Recent studies have shown that iron homeostasis plays an important role in the regulation of immune responses, and imbalance of iron homeostasis may affect the development, function, and death of immune cells [66]. The immune infiltration analysis in our study showed a significantly increased proportion of CD8 + T cells and a significantly decreased proportion of neutrophils in IPAH lung samples, which is consistent with previous reports [64,67,68]. Although there was a linear correlation between some key genes and CD8 + T cells and neutrophils, this correlation did not show consistency in both datasets, which may be due to the limitation or individual differences of the regulation of immune infiltration in IPAH by IMRGs, and the regulation of iron metabolism in IPAH on immune infiltration requires further research.
The exploration of effective target therapeutics based on genes that play a key role in pathology has always been the focus of researchers [25,69]. According to the key genes we identified, we predicted several potential targeting drugs, especially celastrol and cinnamaldehyde, which showed high drug-targeting correlations. Cinnamaldehyde treatment can inhibit MCT-induced elevation in right ventricle systolic pressure, RV/LV + S, and right ventricular collagen accumulation. Celastrol treatment can ameliorate right ventricular systolic pressure, hypertrophy, fibrosis, and dysfunction in hypoxia-induced PAH in mice and SU5416/hypoxiainduced PAH in rats. Although both drugs were identified to be protective against PAH, modulation of iron metabolism as its potential functional mechanism has not been explored; further experimental clarification is needed.
To define the role of dysregulated iron metabolism in IPAH, further validation of our results in an appropriate animal model is necessary but difficult. Most current animal models of PAH have been constructed by chemical induction, chronic hypoxia, or surgery. Due to the considerable pathological differences between different species of PAH, although these animal models morphologically reproduce the features of human PAH, there are currently no available animal models that well reproduce the histological features and natural history of IPAH, which makes the validation potentially inaccurate and even contradictory conclusions. Morphological research and validation of protein expression levels on large human samples are urgently needed for further studies.

Conclusion
We identified DEIMRGs in normal and IPAH lung samples and analyzed their potential regulatory mechanisms and further identified key genes. In addition, we found that IMRGs may be regulated by miRNAs and then identified crucial miRNA-IMRG regulatory networks. These findings contribute to a deeper understanding of the unique role of dysregulated iron metabolism in IPAH, and in-depth studies of IMRG may provide potential therapeutic targets and biomarkers for IPAH patients, yet further studies are needed to analyze the complex regulatory mechanisms.

Supplementary Materials
Supplementary materials are available online at DOI: 10.6084/m9.figshare.14877513. Figure S1: gene expression vioplot of GSE117261 and GSE15197 after normalization. Figure S2: correlation heat map of differentially expressed iron metabolism-related genes in GSE117261. Figure S3: predicted target genes of downregulated miRNA. Figure S4: predicted target genes of upregulated miRNA. Figure S5: key modules identified by the Cytoscape plugin MCODE. Table  S1: the merged iron metabolism-related gene set. Figure S6: correlation heat map of immune cells in GSE117261 and GSE15197. Figure S7: linear regression analysis between expression of key genes and the proportion of immune cells in GSE117261 and GSE15197. Figure S8: top 10 targeted drugs predicted in the DSigDB database ranked by FDR. Table S1: the merged iron metabolism related gene set. Table  S2: dysregulated miRNAs in IPAH samples. Table S3: differ-entially expressed iron metabolism-related gene set. Table  S4: rank values of differentially expressed iron metabolismrelated genes by MCC algorithm. Table S5: the proportion of infiltrating immune cells estimated by the CIBERSORT algorithm in GSE117261. Table S6: the proportion of infiltrating immune cells estimated by the CIBERSORT algorithm in GSE15197. Table S7: predicted target drug using the DSigDB database. (Supplementary Materials)